Quantitative Infection Dynamics of Cafeteria Roenbergensis Virus
The discovery of giant viruses in unicellular eukaryotic hosts has raised new questions on the nature of viral life. Although many steps in the infection cycle of giant viruses have been identified, the quantitative life history traits associated with giant virus infection remain unknown or poorly c...
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doaj-1dd310218a10459193c46109f4e5d48b2020-11-24T21:47:44ZengMDPI AGViruses1999-49152018-08-0110946810.3390/v10090468v10090468Quantitative Infection Dynamics of Cafeteria Roenbergensis VirusBradford P. Taylor0Joshua S. Weitz1Corina P. D. Brussaard2Matthias G. Fischer3Program for Computational Biology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USASchool of Biological Sciences and School of Physics, Georgia Institute of Technology, Atlanta, GA 30332, USADepartment of Marine Microbiology and Biogeochemistry, NIOZ Royal Netherlands Institute of Sea Research, and University of Utrecht, P.O. Box 59, 1790 AB Den Burg, Texel, The NetherlandsDepartment of Biomolecular Mechanisms, Max Planck Institute for Medical Research, 69120 Heidelberg, GermanyThe discovery of giant viruses in unicellular eukaryotic hosts has raised new questions on the nature of viral life. Although many steps in the infection cycle of giant viruses have been identified, the quantitative life history traits associated with giant virus infection remain unknown or poorly constrained. In this study, we provide the first estimates of quantitative infection traits of a giant virus by tracking the infection dynamics of the bacterivorous protist Cafeteria roenbergensis and its lytic virus CroV. Leveraging mathematical models of infection, we quantitatively estimate the adsorption rate, onset of DNA replication, latency time, and burst size from time-series data. Additionally, by modulating the initial ratio of viruses to hosts, we also provide evidence of a potential MOI-dependence on adsorption and burst size. Our work provides a baseline characterization of giant virus infection dynamics relevant to ongoing efforts to understand the ecological role of giant viruses.http://www.mdpi.com/1999-4915/10/9/468giant virusesmultiple infectionsvirus factoriesinfection modelingCroV |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Bradford P. Taylor Joshua S. Weitz Corina P. D. Brussaard Matthias G. Fischer |
spellingShingle |
Bradford P. Taylor Joshua S. Weitz Corina P. D. Brussaard Matthias G. Fischer Quantitative Infection Dynamics of Cafeteria Roenbergensis Virus Viruses giant viruses multiple infections virus factories infection modeling CroV |
author_facet |
Bradford P. Taylor Joshua S. Weitz Corina P. D. Brussaard Matthias G. Fischer |
author_sort |
Bradford P. Taylor |
title |
Quantitative Infection Dynamics of Cafeteria Roenbergensis Virus |
title_short |
Quantitative Infection Dynamics of Cafeteria Roenbergensis Virus |
title_full |
Quantitative Infection Dynamics of Cafeteria Roenbergensis Virus |
title_fullStr |
Quantitative Infection Dynamics of Cafeteria Roenbergensis Virus |
title_full_unstemmed |
Quantitative Infection Dynamics of Cafeteria Roenbergensis Virus |
title_sort |
quantitative infection dynamics of cafeteria roenbergensis virus |
publisher |
MDPI AG |
series |
Viruses |
issn |
1999-4915 |
publishDate |
2018-08-01 |
description |
The discovery of giant viruses in unicellular eukaryotic hosts has raised new questions on the nature of viral life. Although many steps in the infection cycle of giant viruses have been identified, the quantitative life history traits associated with giant virus infection remain unknown or poorly constrained. In this study, we provide the first estimates of quantitative infection traits of a giant virus by tracking the infection dynamics of the bacterivorous protist Cafeteria roenbergensis and its lytic virus CroV. Leveraging mathematical models of infection, we quantitatively estimate the adsorption rate, onset of DNA replication, latency time, and burst size from time-series data. Additionally, by modulating the initial ratio of viruses to hosts, we also provide evidence of a potential MOI-dependence on adsorption and burst size. Our work provides a baseline characterization of giant virus infection dynamics relevant to ongoing efforts to understand the ecological role of giant viruses. |
topic |
giant viruses multiple infections virus factories infection modeling CroV |
url |
http://www.mdpi.com/1999-4915/10/9/468 |
work_keys_str_mv |
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